Article
PERSONALIZED E-LEARNING COURSE RECOMMENDATION SYSTEM
In today’s digital age, the rapid growth of online educational platforms has created an overwhelming abundance of courses across diverse domains. However, the lack of personalized guidance often leaves learners confused and struggling to choose the right courses that align with their individual goals, interests, and skill levels. To address this challenge, this project proposes the development of a Personalized E-Learning Course Recommendation System that leverages artificial intelligence and machine learning techniques to provide tailored course suggestions to users. The system collects user-specific data such as educational background, learning preferences, previously completed courses, user ratings, and feedback, and utilizes this information to generate accurate and relevant course recommendations. It employs a hybrid recommendation engine combining content-based filtering, which matches courses based on user preferences and course metadata, and collaborative filtering, which identifies patterns in user behavior and suggests courses liked by similar users. This ensures a highly customized learning experience that evolves over time with continuous feedback and usage. The platform also integrates a userfriendly interface, a smart search feature, progress tracking, and an optional admin panel for managing course content. By intelligently matching learners with suitable courses, the system not only enhances user engagement and satisfaction but also significantly improves the effectiveness of the learning process, making education more accessible, efficient, and impactful.
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